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Influence and Information Flow in Online Social Networks

Influence and Information Flow in Online Social Networks

Afrand Agah, Mehran Asadi
Copyright: © 2021 |Pages: 19
ISBN13: 9781799890201|ISBN10: 1799890201|EISBN13: 9781799890218
DOI: 10.4018/978-1-7998-9020-1.ch026
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MLA

Agah, Afrand, and Mehran Asadi. "Influence and Information Flow in Online Social Networks." Research Anthology on Strategies for Using Social Media as a Service and Tool in Business, edited by Information Resources Management Association, IGI Global, 2021, pp. 502-520. https://doi.org/10.4018/978-1-7998-9020-1.ch026

APA

Agah, A. & Asadi, M. (2021). Influence and Information Flow in Online Social Networks. In I. Management Association (Ed.), Research Anthology on Strategies for Using Social Media as a Service and Tool in Business (pp. 502-520). IGI Global. https://doi.org/10.4018/978-1-7998-9020-1.ch026

Chicago

Agah, Afrand, and Mehran Asadi. "Influence and Information Flow in Online Social Networks." In Research Anthology on Strategies for Using Social Media as a Service and Tool in Business, edited by Information Resources Management Association, 502-520. Hershey, PA: IGI Global, 2021. https://doi.org/10.4018/978-1-7998-9020-1.ch026

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Abstract

This article introduces a new method to discover the role of influential people in online social networks and presents an algorithm that recognizes influential users to reach a target in the network, in order to provide a strategic advantage for organizations to direct the scope of their digital marketing strategies. Social links among friends play an important role in dictating their behavior in online social networks, these social links determine the flow of information in form of wall posts via shares, likes, re-tweets, mentions, etc., which determines the influence of a node. This article initially identities the correlated nodes in large data sets using customized divide-and-conquer algorithm and then measures the influence of each of these nodes using a linear function. Furthermore, the empirical results show that users who have the highest influence are those whose total number of friends are closer to the total number of friends of each node divided by the total number of nodes in the network.

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